Title: How reliable are mathematical model predictions if their equations are uncertain? 

Abstract: Uncertainty quantification (UQ) is the science of characterizing, quantifying, and reducing uncertainties in mathematical models. It is critical for informing decisions, because it provides a measure of confidence in model predictions, given the uncertainties present in the model. While approaches to characterize uncertainties in model parameters, boundary and initial conditions are well established, it is less clear how to address uncertainties arising when the equations of a mathematical model are themselves uncertain—that is, when there is model-form uncertainty. Model-form uncertainty often arises in models of complex physical phenomena where (1) simplifications for computational tractability or (2) lack of knowledge lead to unknowns in the governing equations for which appropriate mathematical forms are unknown or may not exist. In this talk, I briefly introduce major concepts in UQ, then I discuss approaches to characterize model-form uncertainty and its impact on model predictions.